Instructions to use sylvain471/beit_doc_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sylvain471/beit_doc_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sylvain471/beit_doc_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("sylvain471/beit_doc_classification") model = AutoModelForImageClassification.from_pretrained("sylvain471/beit_doc_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sylvain471/beit_doc_classification: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/sylvain471/beit_doc_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sylvain471/beit_doc_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sylvain471/beit_doc_classification/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- b453d7ccdda6aade88571b0bb0653b82e46eb79c0f2a1d7ebd2268be98a30436
- Size of remote file:
- 343 MB
- SHA256:
- 0ccb8eff7ac20c117d82eebd69a252d335d1dc9b7c33d76112de2bd5f762cb98
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